Triple

T19420588
Position Surface form Disambiguated ID Type / Status
Subject Orange Free State Province E485840 entity
Predicate containsCity P294 FINISHED
Object Sasolburg NE NERFINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Sasolburg | Statement: [Orange Free State Province, containsCity, Sasolburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sasolburg
Context triple: [Orange Free State Province, containsCity, Sasolburg]
  • A. Sasolburg chosen
    Sasolburg is an industrial town in South Africa’s Free State province, known primarily for its large petrochemical complex and proximity to the Vaal River.
  • B. Rustenburg
    Rustenburg is a city in South Africa’s North West Province known for its mining industry and as one of the venues for the 2010 FIFA World Cup.
  • C. Johannesburg–Sishen
    Johannesburg–Sishen is a domestic air route in South Africa linking the major city of Johannesburg with the mining town of Sishen in the Northern Cape.
  • D. Bothasig
    Bothasig is a residential suburb in the northern part of Cape Town, South Africa.
  • E. Cornberg
    Cornberg is a small municipality in the German state of Hesse, known for its rural setting and historical monastery complex.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d8e8d688f881909c85104a62e09d8a completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63214d768819082129100d7116521 completed April 20, 2026, 2:03 p.m.
Created at: April 10, 2026, 1:37 p.m.